Multi-Information Model for Large-Flowered Chrysanthemum Cultivar Recognition and Classification.

Abstract

The traditional Chinese large-flowered chrysanthemum is one of the cultivar groups of chrysanthemum ( Chrysanthemum × morifolium Ramat.) with great morphological variation based on many cultivars. Some experts have established several large-flowered chrysanthemum classification systems by using the method of comparative morphology. However, for many cultivars, accurate recognition and classification are still a problem. Combined with the comparative morphological traits of selected samples, we proposed a multi-information model based on deep learning to recognize and classify large-flowered chrysanthemum. In this study, we collected the images of 213 large-flowered chrysanthemum cultivars in two consecutive years, 2018 and 2019. Based on the 2018 dataset, we constructed a multi-information classification model using non-pre-trained ResNet18 as the backbone network. The model achieves 70.62% top-5 test accuracy for the 2019 dataset. We explored the ability of image features to represent the characteristics of large-flowered chrysanthemum. The affinity propagation (AP) clustering shows that the features are sufficient to discriminate flower colors. The principal component analysis (PCA) shows the petal type has a better interpretation than the flower type. The training sample processing, model training scheme, and learning rate adjustment method affected the convergence and generalization of the model. The non-pre-trained model overcomes the problem of focusing on texture by ignoring colors with the ImageNet pre-trained model. These results lay a foundation for the automated recognition and classification of large-flowered chrysanthemum cultivars based on image classification.

Plant phenotyping relevance

画像と比較形態情報を用いてキク品種を認識・分類する深層学習モデルを開発し、別年データで性能評価している。花色や花弁型など観察可能な植物形態の抽出・表現も検討されており、手法が研究の中心である。

Code and data availability

The paper's chrysanthemum image datasets (2018 training/validation and 2019 test sets) and trained multi-output ResNet18 model are not deposited in any public repository. The data availability statement only promises data from the authors upon request. The online supplementary material (cultivar, petal type, and flower

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